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A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

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  • Lei Si
  • Xiangxiang Xiong
  • Zhongbin Wang
  • Chao Tan

Abstract

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

Suggested Citation

  • Lei Si & Xiangxiang Xiong & Zhongbin Wang & Chao Tan, 2020. "A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, March.
    • Handle: RePEc:hin:jnlmpe:2616510
    DOI: 10.1155/2020/2616510
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